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					Assimilation of satellite data for mesoscale
                 modeling




        John. P. George and Munmun Das Gupta




National Centre for Meduim Range Weather Forecasting (NCMRWF)
     (Department of Science and Technology, Govt. of India)
                 Noida (U.P) , INDIA-201307
OUTLINE




1. NCMRWF models and data assimilation system


2. Regional Data Assimilation System at NCMRWF


3. Assimilation-forecast experiments with different satellite
   data
National Centre for Medium Range Weather
Forecasting (NCMRWF) is the premier institution in
India to provide Medium Range Weather Forecasts through
deterministic methods and to render Agro Advisory Services
(AAS) to the farmers.


Application Researh on:
* Numerical Weather Prediction
* Diagnostic studies
* Crop Weather Modeling
* Computer Science.
    NWP Models at NCMRWF
•   Global Model
     T80 at 150x150 km resolution [Operational]
     T170 at 75x75 km resolution [Experimental]



•   Mesoscale Models

       MM5 (Nested 90, 30, 10 km)[Operational]
       ETA at 48 km resolution [Operational]

       Regional Spectral Model at 50 km resolutio
        [Experimental]
                         Aircraft             RS/RW

              Pilot                                  Surface
                                                                  Global Data assimilation System
             Balloon
                                    GTS
                                                   observations   (GDAS) operational at NCMWRF
             Satellite
                                    DATA             Ships &
             data                                     Buoys

                  RTH                      FTP Satellite Data
                                                                  • 6-hrly intermittent
                NEW DELHI                  MSMR, SSMI etc.
                                                                  • 3D-VAR analysis (SSI)
                          DATA RECEPTION &
                              DECODING                            • conventional and satellite obs.
                          at NCMRWF(1/2 hrly)




                                           DATA PROCESSING AND
        PREVIOUS (6 HOURS)
                                             QUALITY CONTROL
                                                                        Satellite data assimilated at
             ANALYSIS
                                                                        NCMRWF
Repeated                                                                • CMVs (AMVs) from GOES,
four times
        PREVIOUS (6HR.)                                                 METEOSAT,GMS and Kalpana/INSAT
                                    DATA PROCESSING
a day      ANALYSIS     SSI ANALYSIS       &
                                                SURFACE
00,06,12 &                          QUALITY CONTROL
                                               BOUNDARY
                                                                        • High resolution winds from
18 UTC                                         CONDITIONS               METEOSAT-5(63ºE)
                                                 SURFACE
                               ANALYSIS
                     T80 GLOBAL SPECTRAL        BOUNDARY
                                               CONDITIONS
                                                                        • ATOVS (120km) temperature and
                       FORECAST MODEL
                                                                        humidity profiles
                                                                        • SSM/I wind speed
                                                                        • QSCAT winds
                   MEDIUM RANGE WEATHER FORECAST
                       BASED ON 00 UTC ANALYSIS
       Global observations received at NCMRWF
        (through GTS and ftp) in January-2005

Platform                   Observations received at
                           NCMRWF per day
SYNOP (Land)               24785
SHIP                       6451
BUOY                       12511
AIREP                      2756
TEMP                       1057
SATOB
400-150 hPa                8954
1000-700 hPa               5571
ATOVS                      32000 (approx.)
SSMI                       100000 (approx.)
Comparison of global observations received at NCMRWF
 (through GTS and ftp) & ECMWF in January-2005


   Platform         Observations received at   ECMWF
                    NCMRWF per day
   SYNOP (Land)     24785                      57980

   SHIP             6451                       6872

   BUOY             12511                      19000

   AIREP            2756                       57469

   TEMP             1057                       1181

   SATOB                                       418526
   400-150 hPa      8954                       231535
   1000-700 hPa     5571


   ATOVS            32000 (approx.)            600000 (approx.)

   SSMI             100000 (approx.)
Regional Assimilation- Forecast system at NCMRWF




 - MM5 Model (since 2002- with interpolated global
model analysis) / WRF(2005)


 - NCAR 3DVAR (2005)
Regional Assimilation- Forecast system at NCMRWF

NCMRWF is running MM5 Model (NCAR) in real time basis
since 2002
Domain:
 Horizontal (Triple Nested)
 Vertical: 23 Levels (Sigma-Hybrid)
 Time Steps:      Domain-1: 270 S,
                   Domain-2: 90 S,
                   Domain-3 &4: 30 S
 Topography:      USGS (Interpolated depending on resolution)
 Vegetation/ Land use: 25 Categories (USGS)
Initial and lateral boundary conditions are from NCMRWF’s global model
Boundary conditions are updated every 12 hours.
NCAR MM5/WRF-3DVAR at NCMRWF
MM5-3DVAR system mainly consists of the following four
components
(a)   Background Pre-processing
(b)   Observation Pre-processing and quality control
(c)   Variational Analysis
(d)   Updation of Boundary Conditions

                                         3DVAR has been
                                         implemented as 6-hrly
                                         intermittent scheme
                                         with ±3UTC window

       (one time)
(a) Background Preprocessing
-Terrain (Defines domain, orography, land use etc.)
-Pregrid (reads background forecast)
-Regridder (Horizontal interpolation of background forecast)
-Interpf (Vertical interpolation of background forecast)
(b) Observation Preprocessing
Observation Preprocessor prepares the observation in a form
which can be ingest into 3DVAR
- Preparation of Background error covarience statistics
(Once)
(c ) 3DVAR
(d) Update the boundary condition -Using new analysis
              Overview of MM5/WRF 3DVAR

                 Namelist
                                      Xb                BE            Yo
                   File



                                                        Setup
  3DVAR           Read               Setup                           Setup
                                                     Background
  START          Namelist         Background                      Observations
                                                       Errors

                                                                   Calculate
 Compute
                            Minimise Cost Function                  (O – B)
 Analysis

                                  Outer Loop
 Calculate                         Output                           3DVAR
Diagnostics                        Analysis                          END



Diagnostic
                                     Xa
   File
Basic aim of MM5/WRF - 3DVAR is to produce an optimal
analysis through iterative solution of

                1                       1
                            1
J ( x)  J  J  ( x  x ) B ( x  x )  ( y  y o )T ( E  F ) 1 ( y  y o )
         b     0        b T         b

                2                       2



 where x analysis state
        xb , background
        yo observation
         y =H(x )
  B, E and F are the background, observation (instrumental) and
 representivity error covariance matrices respectively
Practical implementation of 3DVAR requires simplifications

   –   Simplified error covariances.
   –   Linearized observation operators, balance equation.
   –   Thinning of observations.
   –   Suitable choice of analysis control variables
   –   etc.
Control variables
In MM5/WRF there are three choices for the control variable
cv_option = 1
         U-component of wind
         V-component of wind
         Temperature
         Pressure
         Moisture variable as specific humidity or relative humidity
cv_option = 2
         Stream function (ψ)
         Velocity potential (χ)
        Unbalanced part of pressure (Pu)
         Moisture variable as specific humidity or relative humidity
cv_option = 3
         Stream function (ψ)
         Unbalanced part of velocity potential (χu)
         Unbalanced part of temperature (Tu)
         Log of surface pressure
         Pseudo relative humidity
About the Experiment
MM5/WRF-3DVAR assimilation cycle (6 hr intermittent) has been
run a period of 12 days (0006UTC 21st - 0000 UTC31st July 2004)
Conventional data such as SYNOP, SHIP, BUOY, AIREP,
AMDAR, TEMP, PILOT and SATOB used (CRTL)



ATOVS Temperature & Humidity profile
SSM/I      Sea surface wind speed &
          Total precipitable water vapor
QSCAT Sea surface wind direction and speed
GPS-RO     Refractivity profile (During March-2004)
Coverage of conventional data used in the assimilation cycle




        SYNOP                       BUOY




      PILOT                       TEMP
Variation of RMSE
over the cyclic
assimilation period
(21st –31st July
2004) of
 Back ground –
Observation (OI)
& Analysis –
Observation (AO)
computed against
RS/RW data
Variation of Bias
over the cyclic
assimilation
period (21st -31st
July 2004) of


Back ground -
Observation (OI)
& Analysis -
Observation
(AO) computed
against RS/RW
data
METEOSAT -5
00 UTC image
27 July 2004(L)
28 July 2004(R)
29 July 2004(B)
Coverage of ATOVS data on a typical day
Analysed height and wind fields for CRTL & ATOVS run 850 hPa
                00UTC 27th 28th 29th July 2004
24, 48 and 72 hr. forecasts of ht. & wind fields for CRTL & ATOVS run
              850 hPa based on 00UTC 26th July 2004
Results


     In ATOVS run, the centre of circulation in wind field
     coincide the centre of low in height field

     Utilisation of ATOVS- Improves the track prediction
Coverage of SSSM/I data
Analysed height and wind fields for CRTL   24, 48 and 72 hr. forecasts of ht. & wind
& SSM/I run 850 hPa 00UTC 27th 28th        fields for CRTL & SSM/I run 850 hPa
29th July 2004                             based on 00UTC 26th July 2004
Results

      Winds over Bay of Bengal are stronger in
      SSMI analysis

     Though the position of the system in
     forecasts are not very different in CTRL
     and SSMI run, but the intensity of the
     system is stronger in SSMI
Coverage of QSCAT data on a typical day
Analysed height and wind fields for CRTL   24, 48 and 72 hr. forecasts of ht. & wind
& QSCAT run 850 hPa 00UTC 27th 28th        fields for CRTL & QSCAT run 850
29th July 2004                             hPa based on 00UTC 26th July 2004
Results


Structure of the cyclonic system over Bay of Bengal region in
QSCAT analysis is better defined and also stronger than that of
CTRL analysis at 850 hPa.


The system is predicted much stronger in QSCAT run
(forecast) compared to that of CTRL. This emphasize that
the further tuning is required before utilising QSCAT data in
the assimilation system
Assimilation of Global Positioning System (GPS) data
  The GPS consists of a constellation of satellites which transmit on
two L-band frequencies (1575.42 MHz for L1 and 1227.6 MHz for L2).


  These two signals are delayed as they propagate through the
atmosphere due to the presence of atmospheric water vapor. This "wet
delay" is detectable from the GPS phase observations at the fixed
ground receiver stations and can be transformed into an estimate of
the perceptible water vapor (PW) present in the troposphere above
that location.


    GPS radio occultation technique - When radio waves from the GPS
satellite (L1 & L2) pass through the atmosphere, either during a rise
event or a set event as seen from the receiver on the low earth orbit
(LEO) satellite, they are refracted through an angle determined by the
refractivity gradients along the path. These, in turn, depend on the
gradients of air density (and hence temperature), water vapor and
electron density.
  GPS Radio Occultation Measurements & Processing




Phase& Amplitude of the Signal ---> Bending Angle ---> Refractivity ---> T & Q
GPS RO Measurement & Processing
Characteristics of GPS Radio Occultation (RO) Data

 Global 3-D coverage

 High accuracy

 High vertical resolution (~ 100 m in lower troposphere)

 All weather-minimally affected by aerosols, clouds or prec.

 Independent height and pressure

 Requires no first guess sounding

 Independent of radiosonde calibration

 No instrument drift
 Compact, low-power, low-cost sensor
 No satellite-to-satellite bias
����


There are considerable uncertainties in global analyses over data
void regions (e.g., where there are few or no radiosondes), despite
the fact that most global analyses now make use of satellite
observations.


   GPS RO missions (such as COSMIC) can be designed to have
globally uniform distribution (not limited by oceans, or high
topography).


���� The accuracy of GPS RO is compatible or better than
radiosonde and can be used to calibrate other observing systems.

����
3DVAR Assimilation Experiment - CHAMP RO Data over Indian Region

  (a)                                 (b)




500hPa (a) wind &height and (b) Relative humidity increments of NCAR-
3DVAR assimilation (18UTC13 March 2004) with GPS refractivity data
3DVAR Assimilation Experiment - CHAMP RO Data over Indian Region
 Table.(a). OB (Observation-Background) statistics of GPS refractivity (CHAMP
 Data) assimilation - NCAR-3DVAR (Background - NCMRWF T80 analysis)
 (a)                     Minimum        Maximum     Average      RMSE
                         (O-B)          (O-B)       (O-B)        (O-B)

 18UTC13 March2004       -6.4517        2.6651      -0.7472      1.8631

 18UTC15 March2004       -2.8321        12.2672     2.0387       3.7346


Table.(b). AO (Analysis-Observation) statistics of GPS refractivity (CHAMP
Data) assimilation - NCAR-3DVAR
 (b)                     Minimum        Maximum     Average      RMSE
                         (A-O)          (A-O)       (A-O)        (A-O)
 18UTC13 March2004       -2.0816        1.9913      -0.0012      0.5038

 18UTC15 March2004       -3.6446        3.6123      0.7128       1.6121


*Assimilation of GPS refractivity profile shows improvement in AO
Results

   Small scale wind features are more prominent in CTRL analysis
    compared to interpolated global analysis (IGLB)

    Mismatch between circulation center in wind and height filed, as
    seen in case of Bay of Bengal circulation, in CTRL analyses is
    reduced considerably with utilization of ATOVS data

 SSM/I and QSCAT data intensify the circulation in Bay of Bengal
  both in analysis as well in forecast (unrealistic). This emphasizes
  the need of proper tunings before assimilation of these data.

 Assimilation of GPS refractivity profile shows improvement in
  AO
Expected observations from Megha-tropique satellite
Megha-tropique satellite is proposed to carry three scientific instruments:

Multi-frequency Microwave Scanning Radiometer, MADRAS

•      Surface winds
•      Ocean rain
•      Cloud liquid water content
•      Deep convective areas
•      Cloud top ice, anvil areas

Multi-channel Microwave Instrument, SAPHIR
•      Humidity profile

Multi-channel instrument, SCARAB
•      Radiation budget measurments…Total and SW
radiation measurements
MADRAS is a microwave imager, with conical scanning
(incidence angle 56°). The main aim of the mission being the study
of cloud systems, a 157 GHz channel is present in order to study the
high level ice clouds associated with the convective systems.

Frequencies        Polarization Pixel Size Main use


18.7 Ghz ± 100     H+V           40 km       ocean rain and surface
Mhz                                          wind
23.8 Ghz ± 200     V             40 km       integrated water vapor
Mhz
36.5 Ghz ± 500     H+V           40 km       cloud liquid water, precip.
Mhz
89 Ghz ± 1000 Mhz H + V          10 km       deep convection areas

157 Ghz ± 1000     H+V            6 km       cloud top ice, anvil areas
Mhz
SAPHIR is a sounding instrument with 6 channels near the
absorption band of water vapor at 183 Ghz. These channels
provide relatively narrow weighting functions from the
surface to about 10 km

      Central frequency      183 GHz
      Frequency band         ± 10 GHz
      Frequency resolution   50 MHz to 1 GHz for 6 channels



ScaRaB is a scanning radiative budget instrument.The
basic measurements of ScaRaB are the radiances in two wide
channels, a solar channel (0.2 - 4 µm), and a total channel
(0.2 - 200 µm)
Following parameters can be used as an input to the
NCMRWF assimilation- forecast system:-

Ocean surface wind, integrated water vapor and ocean
rain (MADRAS)

Water vapor profiles in the cloud free troposphere (SAPHIR).
Use of these parameters in our assimilation system may improve the
distribution of the water vapor over the tropical oceans in our
analysis, which may ultimately improve the convection and other
precipitation processes in the model.

Cloud liquid water and ice (MADRAS) can also be used an
input to the model, which can improve the computation of cloud
optical properties (input to the radiation scheme) in the model and
hence the radiation fluxes and heating/cooling rates.
Shortwave and Longwave radiation (ScaRaB)
measurement can be used for the validation of the radiation
scheme over the tropical areas. The radiative fluxes observation
in this mission is a valuable data to validate the model
generated cloud radiative forcing.


Deep convection areas, cloud liquid water,
precipitation, cloud top ice, anvil areas and humidity
profiles (SAPHIR, MADRAS) can be used for the
validation of the parameterization of convection and other
precipitation processes in the model.
Future Scenario (NWP Global model) :

•Increased horizontal and vertical resolution

•Horizontal resolution: 8-15 km (2015), 3-5 km (2025)

•Vertical resolution:
   •Boundary layer: 70m (2015), 40m (2025)
   •Free atmosphere: 300m (2015), 200m (2025)
   •Stratosphere:     500m (2015), 200m (2025)

				
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